Observer analyses
Hannah, 5/16/2017
Data for Study 1 was collected in Fall 2015. Data for Study 2 was collected in Fall 2016 (hypotheses were preregistered at aspredicted.org).
In Study 1:
- 101 subjects participated
- 8 subjects’ AP data was discarded (see “Study1_badsubsAP.txt”)
- 9 subjects’ WIT data was discarded (see “Study1_badsubsWIT.txt”)
This left 93 subjects with AP data and 92 subjects with WIT data.
Additionally, IMS/EMS data is missing for subs 3, 27, 53, 82, 88, 89, 90, and 101.
In Study 2:
- 206 subjects participated
- 8 subjects’ AP data was discarded (see “Study2_badsubsAP.txt”)
- 7 subjects’ WIT data was discarded (see “Study2_badsubsWIT.txt”)
This left 198 subjects with AP data and 199 subjects with WIT data.
Additionally, IMS/EMS data is missing for 111, 189, and 201.
There were 48 trials for each condition in each task. In Study 2, each task was split into two sections so that participants could answer anxiety questions in the middle and end of each task.
1. Accuracy in each task:
2 (Prime: Black/White) x 2 (Target: gun/tool or positive/negative) rANOVA
Only showing interaction indicating racial bias (Prime x Target). For calculation of effect sizes, see “7 Effect sizes.R”
#### Study 1:
WIT
## Df Sum Sq Mean Sq F value Pr(>F)
## PrimeType:TargetType 1 2883 2882.9 96.5 5.97e-16 ***
## Residuals 91 2719 29.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1AP
## Df Sum Sq Mean Sq F value Pr(>F)
## PrimeType:TargetType 1 1794 1794.3 39.797 1e-08 ***
## PrimeType:TargetType:Observer 1 43 42.5 0.943 0.334
## Residuals 91 4103 45.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Study 2:
Only showing interaction indicating racial bias (Prime x Target). For calculation of effect sizes, see “7 Effect sizes.R”
WIT
## Df Sum Sq Mean Sq F value Pr(>F)
## PrimeType:TargetType 1 3861 3861 120.7 <2e-16 ***
## Residuals 198 6331 32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1AP
## Df Sum Sq Mean Sq F value Pr(>F)
## PrimeType:TargetType 1 2478 2478.3 54.86 3.71e-12 ***
## Residuals 197 8900 45.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 12. Comparing accuracy across tasks
- Correlation between performance bias scores
- Look at 3 way Prime x Target x Task interaction
Study 1
Excludes subjects that don’t have data in both tasks (only includes sample of 90).
A. Correlation between performance bias scores on each task
##
## Call:
## lm(formula = APStand ~ WITStand, data = s1.perfBias)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.25273 -0.64697 -0.04138 0.68037 2.40625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.621e-17 1.042e-01 0.000 1.0000
## WITStand 1.851e-01 1.048e-01 1.767 0.0807 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9883 on 88 degrees of freedom
## Multiple R-squared: 0.03427, Adjusted R-squared: 0.02329
## F-statistic: 3.123 on 1 and 88 DF, p-value: 0.08068B. Examine 3 way Prime x Target x Task interaction
## Saving 7 x 5 in image## Df Sum Sq Mean Sq F value Pr(>F)
## PrimeType:ConType:Task 1 992 991.7 23.04 6.35e-06 ***
## Residuals 89 3830 43.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Study 2
Excludes subjects that don’t have data in both tasks (only includes sample of 195).
A. Correlation between performance bias scores on each task
##
## Call:
## lm(formula = APStand ~ WITStand, data = s2.perfBias)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1856 -0.6008 -0.0491 0.5380 4.4047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.394e-16 6.594e-02 0.000 1
## WITStand 4.065e-01 6.611e-02 6.149 4.46e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.916 on 191 degrees of freedom
## Multiple R-squared: 0.1653, Adjusted R-squared: 0.1609
## F-statistic: 37.82 on 1 and 191 DF, p-value: 4.456e-09B. Examine 3 way Prime x Target x Task interaction
## Saving 7 x 5 in image## Df Sum Sq Mean Sq F value Pr(>F)
## PrimeType:ConType:Task 1 3123 3123.3 94.02 <2e-16 ***
## Residuals 192 6378 33.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13. Look at correlations between PDP estimates
Study 1:
##
## Call:
## lm(formula = WIT_MeanC.stand ~ AP_MeanC.stand, data = s1.widePDP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.77991 -0.52543 0.04554 0.59056 1.68756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.088e-16 8.353e-02 0.000 1
## AP_MeanC.stand 6.157e-01 8.400e-02 7.329 1.06e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7925 on 88 degrees of freedom
## Multiple R-squared: 0.379, Adjusted R-squared: 0.372
## F-statistic: 53.72 on 1 and 88 DF, p-value: 1.062e-10
##
## Call:
## lm(formula = WIT_AResid.stand ~ AP_AResid.stand, data = s1.widePDP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.64807 -0.47586 0.03648 0.71495 2.42030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.771e-17 1.030e-01 0.000 1.0000
## AP_AResid.stand 2.349e-01 1.036e-01 2.267 0.0258 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9775 on 88 degrees of freedom
## Multiple R-squared: 0.05519, Adjusted R-squared: 0.04445
## F-statistic: 5.14 on 1 and 88 DF, p-value: 0.02583
## Saving 7 x 5 in image##
## Call:
## lm(formula = WITestimate ~ APTestimate * Type, data = compareAC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.64807 -0.49558 0.03792 0.65228 2.42030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.629e-16 9.380e-02 0.000 1.00000
## APTestimate 2.349e-01 9.432e-02 2.491 0.01368 *
## TypePDP-C 5.879e-16 1.326e-01 0.000 1.00000
## APTestimate:TypePDP-C 3.807e-01 1.334e-01 2.854 0.00483 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8898 on 176 degrees of freedom
## Multiple R-squared: 0.2171, Adjusted R-squared: 0.2038
## F-statistic: 16.27 on 3 and 176 DF, p-value: 2.233e-09Study 2:
##
## Call:
## lm(formula = WIT_MeanC.stand ~ AP_MeanC.stand, data = s2.widePDP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.79597 -0.54572 0.03667 0.51197 1.86599
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.645e-16 5.468e-02 0.00 1
## AP_MeanC.stand 6.527e-01 5.482e-02 11.91 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7596 on 191 degrees of freedom
## Multiple R-squared: 0.426, Adjusted R-squared: 0.423
## F-statistic: 141.7 on 1 and 191 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = WIT_AResid.stand ~ AP_AResid.stand, data = s2.widePDP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3346 -0.6315 -0.0074 0.6270 2.3417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.869e-17 6.973e-02 0.000 1.000000
## AP_AResid.stand 2.577e-01 6.991e-02 3.685 0.000298 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9688 on 191 degrees of freedom
## Multiple R-squared: 0.06639, Adjusted R-squared: 0.0615
## F-statistic: 13.58 on 1 and 191 DF, p-value: 0.0002975
## Saving 7 x 5 in image##
## Call:
## lm(formula = WITestimate ~ APTestimate * Type, data = compareAC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.33459 -0.57549 0.01939 0.57615 2.34165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.660e-17 6.266e-02 0.000 1
## APTestimate 2.577e-01 6.282e-02 4.101 5.02e-05 ***
## TypePDP-C -2.384e-16 8.861e-02 0.000 1
## APTestimate:TypePDP-C 3.950e-01 8.884e-02 4.446 1.15e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8705 on 382 degrees of freedom
## Multiple R-squared: 0.2462, Adjusted R-squared: 0.2403
## F-statistic: 41.59 on 3 and 382 DF, p-value: < 2.2e-164. Test Observer x IMS interaction on three criterion (response accuracy bias, PDP-C, and PDP-A)
IMS is standardized for models, although not in plots. Models give unstandardized estimates.
#####Study 1:
Perf bias:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 0.0849414 | 0.0199339 | 4.2611459 | 0.0000348 |
| scale(IMS) | -0.0165254 | 0.0178931 | -0.9235635 | 0.3571219 |
| ObserverPresent | 0.0327737 | 0.0283822 | 1.1547277 | 0.2499454 |
| TaskWIT | 0.0403840 | 0.0281908 | 1.4325217 | 0.1539701 |
| scale(IMS):ObserverPresent | 0.0140434 | 0.0295156 | 0.4757948 | 0.6348777 |
| scale(IMS):TaskWIT | 0.0264328 | 0.0253047 | 1.0445810 | 0.2978122 |
| ObserverPresent:TaskWIT | -0.0526667 | 0.0401385 | -1.3121244 | 0.1913812 |
| scale(IMS):ObserverPresent:TaskWIT | -0.0166459 | 0.0417413 | -0.3987865 | 0.6905890 |
PDP-C estimates:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 0.3643438 | 0.0316063 | 11.5275674 | 0.0000000 |
| scale(IMS) | 0.0256741 | 0.0283705 | 0.9049588 | 0.3668650 |
| ObserverPresent | 0.0206224 | 0.0450014 | 0.4582618 | 0.6473940 |
| TaskWIT | 0.0104926 | 0.0446981 | 0.2347437 | 0.8147116 |
| scale(IMS):ObserverPresent | -0.0285231 | 0.0467985 | -0.6094881 | 0.5430764 |
| scale(IMS):TaskWIT | -0.0269280 | 0.0401219 | -0.6711537 | 0.5031026 |
| ObserverPresent:TaskWIT | -0.0364566 | 0.0636417 | -0.5728422 | 0.5675659 |
| scale(IMS):ObserverPresent:TaskWIT | 0.0298970 | 0.0661831 | 0.4517325 | 0.6520812 |
PDP-A estimates:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 0.0028778 | 0.0232003 | 0.1240425 | 0.9014393 |
| scale(IMS) | 0.0008643 | 0.0208251 | 0.0415018 | 0.9669483 |
| ObserverPresent | 0.0143705 | 0.0330329 | 0.4350368 | 0.6641294 |
| TaskWIT | 0.0236290 | 0.0328102 | 0.7201722 | 0.4724830 |
| scale(IMS):ObserverPresent | 0.0443437 | 0.0343520 | 1.2908630 | 0.1986368 |
| scale(IMS):TaskWIT | 0.0272456 | 0.0294511 | 0.9251118 | 0.3563185 |
| ObserverPresent:TaskWIT | -0.0721764 | 0.0467155 | -1.5450194 | 0.1243422 |
| scale(IMS):ObserverPresent:TaskWIT | -0.1143868 | 0.0485810 | -2.3545568 | 0.0197741 |
# Look at each level of observer separately
## Present
lm(AResid ~ scale(IMS)*Task, data = s1.wide[s1.wide$Observer == "Present",]) %>%
summary()##
## Call:
## lm(formula = AResid ~ scale(IMS) * Task, data = s1.wide[s1.wide$Observer ==
## "Present", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39115 -0.09930 0.01505 0.10830 0.31012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01413 0.02355 0.600 0.5502
## scale(IMS) 0.03902 0.02370 1.647 0.1036
## TaskWIT -0.04254 0.03331 -1.277 0.2053
## scale(IMS):TaskWIT -0.07522 0.03351 -2.244 0.0276 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1508 on 78 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.07884, Adjusted R-squared: 0.04341
## F-statistic: 2.225 on 3 and 78 DF, p-value: 0.09182## Absent
lm(AResid ~ scale(IMS)*Task, data = s1.wide[s1.wide$Observer == "Absent",]) %>%
summary()##
## Call:
## lm(formula = AResid ~ scale(IMS) * Task, data = s1.wide[s1.wide$Observer ==
## "Absent", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39653 -0.08642 -0.00636 0.09551 0.35744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0029360 0.0230467 0.127 0.899
## scale(IMS) 0.0009669 0.0231851 0.042 0.967
## TaskWIT 0.0254624 0.0325930 0.781 0.437
## scale(IMS):TaskWIT 0.0304796 0.0327887 0.930 0.355
##
## Residual standard error: 0.1494 on 80 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.02973, Adjusted R-squared: -0.00665
## F-statistic: 0.8172 on 3 and 80 DF, p-value: 0.4881# simple slopes
lm(scale(AResid) ~ scale(IMS), data = s1.wide[s1.wide$Observer == "Present" & s1.wide$Task == "APT",]) %>%
summary()##
## Call:
## lm(formula = scale(AResid) ~ scale(IMS), data = s1.wide[s1.wide$Observer ==
## "Present" & s1.wide$Task == "APT", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03036 -0.69571 0.04235 0.46939 2.09364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03924 0.15036 0.261 0.7955
## scale(IMS) 0.26510 0.15223 1.741 0.0895 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9628 on 39 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.07215, Adjusted R-squared: 0.04836
## F-statistic: 3.032 on 1 and 39 DF, p-value: 0.0895lm(scale(AResid) ~ scale(IMS), data = s1.wide[s1.wide$Observer == "Present" & s1.wide$Task == "WIT",]) %>%
summary()##
## Call:
## lm(formula = scale(AResid) ~ scale(IMS), data = s1.wide[s1.wide$Observer ==
## "Present" & s1.wide$Task == "WIT", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2584 -0.4911 0.1650 0.6393 1.6146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04591 0.14301 -0.321 0.750
## scale(IMS) -0.21030 0.14478 -1.452 0.154
##
## Residual standard error: 0.9157 on 39 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.05132, Adjusted R-squared: 0.02699
## F-statistic: 2.11 on 1 and 39 DF, p-value: 0.1544Study 2:
Perf bias:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 0.0606414 | 0.0131239 | 4.6206923 | 0.0000053 |
| scale(IMS) | -0.0090080 | 0.0124408 | -0.7240669 | 0.4694798 |
| ObserverPresent | 0.0332584 | 0.0190066 | 1.7498343 | 0.0809714 |
| TaskWIT | 0.0355513 | 0.0185600 | 1.9154856 | 0.0561970 |
| scale(IMS):ObserverPresent | -0.0159428 | 0.0192768 | -0.8270496 | 0.4087391 |
| scale(IMS):TaskWIT | -0.0101322 | 0.0175940 | -0.5758906 | 0.5650373 |
| ObserverPresent:TaskWIT | -0.0413698 | 0.0268794 | -1.5390890 | 0.1246327 |
| scale(IMS):ObserverPresent:TaskWIT | 0.0277632 | 0.0272615 | 1.0184042 | 0.3091478 |
PDP-C estimates:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 0.3355554 | 0.0233887 | 14.3468823 | 0.0000000 |
| scale(IMS) | 0.0402489 | 0.0221714 | 1.8153523 | 0.0702749 |
| ObserverPresent | -0.0003638 | 0.0338727 | -0.0107404 | 0.9914363 |
| TaskWIT | 0.0595237 | 0.0330767 | 1.7995666 | 0.0727393 |
| scale(IMS):ObserverPresent | -0.0049051 | 0.0343541 | -0.1427815 | 0.8865400 |
| scale(IMS):TaskWIT | -0.0110335 | 0.0313551 | -0.3518874 | 0.7251219 |
| ObserverPresent:TaskWIT | 0.0228862 | 0.0479032 | 0.4777597 | 0.6331019 |
| scale(IMS):ObserverPresent:TaskWIT | -0.0113101 | 0.0485841 | -0.2327951 | 0.8160485 |
PDP-A estimates:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 0.0102392 | 0.0154690 | 0.6619198 | 0.5084324 |
| scale(IMS) | -0.0273694 | 0.0146638 | -1.8664580 | 0.0627639 |
| ObserverPresent | -0.0215384 | 0.0224029 | -0.9614123 | 0.3369694 |
| TaskWIT | -0.0081381 | 0.0218764 | -0.3720030 | 0.7101024 |
| scale(IMS):ObserverPresent | 0.0122012 | 0.0227213 | 0.5369929 | 0.5915935 |
| scale(IMS):TaskWIT | -0.0012590 | 0.0207378 | -0.0607111 | 0.9516219 |
| ObserverPresent:TaskWIT | 0.0176881 | 0.0316825 | 0.5582936 | 0.5769798 |
| scale(IMS):ObserverPresent:TaskWIT | -0.0149861 | 0.0321328 | -0.4663816 | 0.6412154 |
5. Make composite for anxiety in Study 2
Composite is composed of 8 items (standardized before averaged together), alpha = .91
Look at correlation between first and second blocks:
The correlation between the anxiety composite score on the first block and the second block is 0.9098042.
8. Look at relationship between anxiety and PDP-A/PDP-C
Anxiety composite scores were averaged across blocks, so each participant has one anxiety score per task.
## Saving 7 x 5 in image## Linear mixed model fit by REML
## t-tests use Satterthwaite approximations to degrees of freedom ['lmerMod']
## Formula: MeanC ~ scale(Anx_composite) * Task + (1 | Subject)
## Data: s2.wide
##
## REML criterion at convergence: -136.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.09061 -0.49271 0.03496 0.55697 1.95890
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.03381 0.1839
## Residual 0.01764 0.1328
## Number of obs: 386, groups: Subject, 193
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.33925 0.01633 266.70000 20.769 < 2e-16 ***
## scale(Anx_composite) -0.04235 0.01469 377.00000 -2.884 0.00415 **
## TaskWIT 0.06763 0.01355 191.20000 4.991 1.35e-06 ***
## scale(Anx_composite):TaskWIT -0.02641 0.01399 196.70000 -1.889 0.06043 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(A_) TskWIT
## scl(Anx_cm) -0.031
## TaskWIT -0.415 0.058
## sc(A_):TWIT 0.014 -0.455 0.002
## Saving 7 x 5 in image## Linear mixed model fit by REML
## t-tests use Satterthwaite approximations to degrees of freedom ['lmerMod']
## Formula: AResid ~ scale(Anx_composite) * Task + (1 | Subject)
## Data: s2.wide
##
## REML criterion at convergence: -331.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.36687 -0.65794 -0.04769 0.60500 2.60405
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.005595 0.0748
## Residual 0.018353 0.1355
## Number of obs: 386, groups: Subject, 193
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.377e-04 1.115e-02 3.622e+02 -0.039 0.969
## scale(Anx_composite) 1.421e-02 1.096e-02 3.786e+02 1.296 0.196
## TaskWIT 3.064e-03 1.380e-02 1.912e+02 0.222 0.825
## scale(Anx_composite):TaskWIT 1.089e-02 1.403e-02 2.104e+02 0.776 0.438
##
## Correlation of Fixed Effects:
## (Intr) sc(A_) TskWIT
## scl(Anx_cm) -0.033
## TaskWIT -0.619 0.032
## sc(A_):TWIT 0.021 -0.624 0.0019. Look at relationship between IMS and anxiety, as a function of observer
## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing non-finite values (stat_smooth).
## Warning: Removed 6 rows containing missing values (geom_point).## Linear mixed model fit by REML
## t-tests use Satterthwaite approximations to degrees of freedom ['lmerMod']
## Formula: scale(Anx_composite) ~ scale(IMS) * Observer * Task + (1 | Subject)
## Data: s2.wide
##
## REML criterion at convergence: 853.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16647 -0.41242 -0.03104 0.41614 2.71661
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.8195 0.9053
## Residual 0.1543 0.3929
## Number of obs: 380, groups: Subject, 190
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05536 0.10002 217.78000 0.554 0.580487
## scale(IMS) 0.32910 0.09481 217.78000 3.471 0.000625 ***
## ObserverPresent 0.01621 0.14485 217.78000 0.112 0.910999
## TaskWIT -0.06212 0.05631 186.00000 -1.103 0.271398
## scale(IMS):ObserverPresent -0.29884 0.14691 217.78000 -2.034 0.043146 *
## scale(IMS):TaskWIT -0.12604 0.05338 186.00000 -2.361 0.019257 *
## ObserverPresent:TaskWIT -0.03449 0.08155 186.00000 -0.423 0.672846
## scale(IMS):ObserverPresent:TaskWIT 0.15425 0.08271 186.00000 1.865 0.063769 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(IMS) ObsrvP TskWIT sc(IMS):OP s(IMS):T OP:TWI
## scale(IMS) 0.129
## ObsrvrPrsnt -0.690 -0.089
## TaskWIT -0.282 -0.036 0.194
## scl(IMS):OP -0.083 -0.645 -0.030 0.023
## s(IMS):TWIT -0.036 -0.282 0.025 0.129 0.182
## ObsrvP:TWIT 0.194 0.025 -0.282 -0.690 0.008 -0.089
## s(IMS):OP:T 0.023 0.182 0.008 -0.083 -0.282 -0.645 -0.03010. Look at relationship between EMS and anxiety, as a function of observer
## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing non-finite values (stat_smooth).
## Warning: Removed 6 rows containing missing values (geom_point).## Linear mixed model fit by REML
## t-tests use Satterthwaite approximations to degrees of freedom ['lmerMod']
## Formula: scale(Anx_composite) ~ scale(EMS) * Observer * Task + (1 | Subject)
## Data: s2.wide
##
## REML criterion at convergence: 850.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.27546 -0.40978 -0.01832 0.44454 2.53021
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.7796 0.8830
## Residual 0.1591 0.3988
## Number of obs: 380, groups: Subject, 190
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.008509 0.097376 220.150000 0.087 0.930449
## scale(EMS) 0.343958 0.102343 220.150000 3.361 0.000916 ***
## ObserverPresent 0.068828 0.140705 220.150000 0.489 0.625210
## TaskWIT -0.045014 0.056690 186.000000 -0.794 0.428180
## scale(EMS):ObserverPresent -0.142602 0.140972 220.150000 -1.012 0.312859
## scale(EMS):TaskWIT 0.011562 0.059581 186.000000 0.194 0.846342
## ObserverPresent:TaskWIT -0.047481 0.081914 186.000000 -0.580 0.562855
## scale(EMS):ObserverPresent:TaskWIT -0.022435 0.082070 186.000000 -0.273 0.784873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(EMS) ObsrvP TskWIT sc(EMS):OP s(EMS):T OP:TWI
## scale(EMS) -0.006
## ObsrvrPrsnt -0.692 0.004
## TaskWIT -0.291 0.002 0.201
## scl(EMS):OP 0.004 -0.726 0.000 -0.001
## s(EMS):TWIT 0.002 -0.291 -0.001 -0.006 0.211
## ObsrvP:TWIT 0.201 -0.001 -0.291 -0.692 0.000 0.004
## s(EMS):OP:T -0.001 0.211 0.000 0.004 -0.291 -0.726 0.00011. Look at relationship between IMS-EMS and anxiety, as a function of observer
## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
## Linear mixed model fit by REML
## t-tests use Satterthwaite approximations to degrees of freedom ['lmerMod']
## Formula: scale(Anx_composite) ~ scale(IMS.EMS.diff) * Observer * Task + (1 | Subject)
## Data: s2.wide
##
## REML criterion at convergence: 862.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1945 -0.4221 -0.0138 0.4133 2.5022
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.8482 0.921
## Residual 0.1568 0.396
## Number of obs: 380, groups: Subject, 190
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.008219 0.101134 217.250000 0.081 0.935
## scale(IMS.EMS.diff) -0.027382 0.103972 217.250000 -0.263 0.793
## ObserverPresent 0.081193 0.146152 217.250000 0.556 0.579
## TaskWIT -0.052872 0.056492 186.000000 -0.936 0.351
## scale(IMS.EMS.diff):ObserverPresent -0.118671 0.146224 217.250000 -0.812 0.418
## scale(IMS.EMS.diff):TaskWIT -0.094283 0.058077 186.000000 -1.623 0.106
## ObserverPresent:TaskWIT -0.041573 0.081638 186.000000 -0.509 0.611
## scale(IMS.EMS.diff):ObserverPresent:TaskWIT 0.116359 0.081678 186.000000 1.425 0.156
##
## Correlation of Fixed Effects:
## (Intr) sc(IMS.EMS.) ObsrvP TskWIT sc(IMS.EMS.):OP s(IMS.EMS.):T OP:TWI
## sc(IMS.EMS.) 0.086
## ObsrvrPrsnt -0.692 -0.060
## TaskWIT -0.279 -0.024 0.193
## sc(IMS.EMS.):OP -0.061 -0.711 -0.003 0.017
## s(IMS.EMS.):T -0.024 -0.279 0.017 0.086 0.199
## ObsrvP:TWIT 0.193 0.017 -0.279 -0.692 0.001 -0.060
## s(IMS.EMS.):OP: 0.017 0.199 0.001 -0.061 -0.279 -0.711 -0.003